Green public procurement criteria for road infrastructures: State of the art and proposal of a weighted sum multicriteria analysis to assessenvironmental impacts

In the last years, the attention to environmental issue is growing, demonstrating the interest to protect the nature and to better use the non-renewable resources. At international level, and especially in the European Community, for different trades, a wide production of voluntary documents and institutional acts proves the interest and the need for a green economy. An innovative approach may lead to the experience of Green Public Procurements (GPP), to protect the environment as a public interest and to promote technological developments. So far, the experiences of GPP are limited, not entirely positive and in the field of road infrastructures almost entirely absent. Construction and maintenance of road infrastructures is objectively more complex than purchasing goods or services. The paper proposes the integration of the weighted sum multi-criteria analysis into existing procedures. The methodology needs for environmental labels related to materials, machines and works which contribute to the final product "road". The labels are recognized at international level and consistent with procedures, conditions and criteria currently published in road tenders, therefore the approach can be followed to pursue the environmental sustainability of road infrastructures without compromising the economic attention

Space Frequency Codes from Spherical Codes

A new design method for high rate, fully diverse ('spherical') space frequency codes for MIMO-OFDM systems is proposed, which works for arbitrary numbers of antennas and subcarriers. The construction exploits a differential geometric connection between spherical codes and space time codes. The former are well studied e.g. in the context of optimal sequence design in CDMA systems, while the latter serve as basic building blocks for space frequency codes. In addition a decoding algorithm with moderate complexity is presented. This is achieved by a lattice based construction of spherical codes, which permits lattice decoding algorithms and thus offers a substantial reduction of complexity.Comment: 5 pages. Final version for the 2005 IEEE International Symposium on Information Theor

Audio Source Separation Using Sparse Representations

This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 10, pp. 246-264. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch010file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research